Artificial Intelligence in Theater Performance and Physical Training: A Review of Technologies Optimizing Performer Development and Artistic Execution
Keywords:
artificial intelligence, choreography, computer vision, machine learning, movement analysis, performing arts, physical training, pose estimation , technology, theaterAbstract
Objective: Theater performance demands substantial physical capabilities requiring systematic training comparable to athletic preparation. Professional performers engage in cardiovascular conditioning, strength development, and movement technique refinement to meet performance demands. Artificial intelligence technologies, including computer vision, machine learning, and generative modeling, have enabled practical applications in movement analysis, personalized training design, and performance feedback delivery. These advances address accessibility barriers, objective assessment challenges, and individualized program adaptation needs characteristic of theatrical training contexts. This review aimed to (i) examine AI technologies applied in theater performance and physical training contexts, (ii) analyze implementation challenges and limitations across applications, and (iii) identify critical research priorities requiring empirical investigation.
Methods: We searched PubMed, IEEE Xplore, ACM Digital Library, Web of Science, and Google Scholar for studies published 2014-2025. Inclusion criteria required empirical AI applications in theater performance, performing arts training, or physical conditioning relevant to theatrical demands. We extracted data on technologies employed, application contexts, reported outcomes, and identified limitations. Quality assessment examined validation methodology, sample characteristics, and outcome measurement approaches.
Results: Computer vision systems demonstrated validation accuracies with mean errors of 20-30mm in controlled laboratory environments and 50-80 mm in theatrical settings with challenging lighting and costume conditions. Generative choreography systems produced technically coherent movement sequences, receiving mixed artistic evaluations from expert practitioners. Natural language processing achieved 85-92% accuracy for surface-level script sentiment analysis while demonstrating poor performance on dramatic subtext interpretation tasks. AI fitness applications reported initial user engagement improvements, though sustained adherence declined substantially beyond six months across multiple studies. Theater practitioners demonstrated high acceptance (85%) for technical production support applications while expressing concerns (62%) regarding creative process involvement. Research examining long-term effectiveness beyond six months remained critically scarce across all application domains examined.
Conclusion: AI technologies demonstrate potential for technical support and objective assessment in theater and physical training contexts. Successful implementation requires domain-specific design approaches, preservation of human creative agency, and realistic technological capability assessment. Critical research priorities include longitudinal effectiveness validation, diverse population testing, cultural inclusivity in training datasets, and ethical framework development for responsible AI deployment in creative domains.
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